SoroushVahidi/scidocs huggingface dataset:
A practical path loss model for indoor WiFi positioning enhancement
(2007)

Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.
xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs
(2017)

In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED. Keywords— ezfiltration; air-gap; network; optical; covertchannel (key words)
Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals
(2017)

Steganalysis of the quantization index modulation QIM steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization VQ codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network QCCN based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine SVM classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.
Relation Extraction : A Survey
(2017)

With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve access and management of knowledge hidden in large text corpora. Several applications such as Question Answering, Information Retrieval would benefit from this information. Entities like persons and organizations, form the most basic unit of the information. Occurrences of entities in a sentence are often linked through well-defined relations; e.g., occurrences of person and organization in a sentence may be linked through relations such as employed at. The task of Relation Extraction (RE) is to identify such relations automatically. In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques. We also cover the paradigms of Open Information Extraction (OIE) and Distant Supervision. Finally, we describe some of the recent trends in the RE techniques and possible future research directions. This survey would be useful for three kinds of readers i) Newcomers in the field who want to quickly learn about RE; ii) Researchers who want to know how the various RE techniques evolved over time and what are possible future research directions and iii) Practitioners who just need to know which RE technique works best in various settings.
The Treatment of Missing Values and its Effect on Classifier Accuracy
(2004)

The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used most frequently deletes instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve datasets to evaluate the effect on the misclassification error rate of four methods for dealing with missing values: the case deletion method, mean imputation, median imputation, and the KNN imputation procedure. The classifiers considered were the Linear Discriminant Analysis (LDA) and the KNN classifier. The first one is a parametric classifier whereas the second one is a non parametric classifier.
Designing Media Architecture: Tools and Approaches for Addressing the Main Design Challenges
(2016)

Media Architecture is reaching a level of maturity at which we can identify tools and approaches for addressing the main challenges for HCI practitioners working in this field. While previous influential contributions within Media Architecture have identified challenges for designers and offered case studies of specific approaches, here, we (1) provide guidance on how to tackle the domain-specific challenges of Media Architecture design -- pertaining to the interface, integration, content, context, process, prototyping, and evaluation -- on the basis of the development of numerous installations over the course of seven years, and thorough studies of related work, and (2) present five categories of tools and approaches -- software tools, projection, 3D models, hardware prototyping, and evaluation tools -- developed to address these challenges in practice, exemplified through six concrete examples from real-life cases.
More is Less: On the End-to-End Security of Group Chats in Signal, WhatsApp, and Threema
(2018)

Secure instant messaging is utilized in two variants: one-to-one communication and group communication. While the first variant has received much attention lately (Frosch et al., EuroS Cohn-Gordon et al., EuroS Kobeissi et al., EuroS&P17), little is known about the cryptographic mechanisms and security guarantees of secure group communication in instant messaging. To approach an investigation of group instant messaging protocols, we first provide a comprehensive and realistic security model. This model combines security and reliability goals from various related literature to capture relevant properties for communication in dynamic groups. Thereby the definitions consider their satisfiability with respect to the instant delivery of messages. To show its applicability, we analyze three widely used real-world protocols: Signal, WhatsApp, and Threema. By applying our model, we reveal several shortcomings with respect to the security definition. Therefore we propose generic countermeasures to enhance the protocols regarding the required security and reliability goals. Our systematic analysis reveals that (1) the communications' integrity – represented by the integrity of all exchanged messages – and (2) the groups' closeness – represented by the members' ability of managing the group – are not end-to-end protected. We additionally show that strong security properties, such as Future Secrecy which is a core part of the one-to-one communication in the Signal protocol, do not hold for its group communication.
Existential meaning's role in the enhancement of hope and prevention of depressive symptoms.
(2005)

The authors confirmed that existential meaning has a unique relationship with and can prospectively predict levels of hope and depressive symptoms within a population of college students. Baseline measures of explicit meaning (i.e., an individual's self-reported experience of a sense of coherence and purpose in life) and implicit meaning (i.e., an individual's self-reported embodiment of the factors that are normatively viewed as comprising a meaningful life) explained significant amounts of variance in hope and depressive symptoms 2 months later beyond the variance explained by baseline levels of hope/depression, neuroticism, conscientiousness, agreeableness, openness to experience, extraversion, and social desirability. The authors discuss implications of these findings for the field of mental health treatment and suggest ways of influencing individuals' experience of existential meaning.
Transfer learning for class imbalance problems with inadequate data
(2015)

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data are not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.
Player Type Models: Towards Empirical Validation
(2016)

Player type models -- such as the BrainHex model -- are popular approaches for personalizing digital games towards individual preferences of players. Although several player type models have been developed and are currently used in game design projects, there is still a lack of data on their validity. To close this research gap we currently investigate the psychometric properties (factor structure, reliability, stability) and predictive validity (if player type scores can predict player experience) of the player type model BrainHex in an ongoing project. Results of two online studies (n1=592, n2=243) show that the psychometric properties of the BrainHex model could be improved. We suggest to improve the according questionnaire and sketch how the predictive validity could be investigated in future studies.
Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems - A Systematic Review
(2018)

The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.
An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence
(2017)

Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI), which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.
Extensive Analysis and Prediction of Optimal Inventory levels in supply chain management based on Particle Swarm Optimization Algorithm
(2009)

Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain. The dynamic nature of the excess stock level and shortage level from one period to another is a serious issue. In addition, consideration of multiple products and more supply chain members leads to very complex inventory management process. Moreover, the supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. A better optimization methodology would consider all these factors in the prediction of the optimal stock levels to be maintained in order to minimize the total supply chain cost. Here, we are proposing an optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, to overcome the impasse in maintaining the optimal stock levels at each member of the supply chain.
CIO and Business Executive Leadership Approaches to Establishing Company-wide Information Orientation
(2011)

In the digital world, business executives have a heightened awareness of the strategic importance of information and information management to their companies’ value creation. This presents both leadership opportunities and challenges for CIOs. To prevent the CIO position from being marginalized and to enhance CIOs’ contribution to business value creation, they must move beyond being competent IT utility managers and play an active role in helping their companies build a strong information usage culture. The purpose of this article is to provide a better understanding of the leadership approaches that CIOs and business executives can adopt to improve their companies’ information orientation. Based on our findings from four case studies, we have created a four-quadrant leadership-positioning framework. This framework is constructed from the CIO’s perspective and indicates that a CIO may act as a leader, a follower or a nonplayer in developing the company’s information orientation to achieve its strategic focus. The article concludes with guidelines that CIOs can use to help position their leadership challenges in introducing or sustaining their companies’ information orientation initiatives and recommends specific leadership approaches depending on CIOs’ particular situations.
Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis
(2002)

Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF) networks, are used for the analysis of CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.
SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals
(2013)

Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, “from the pocket”. Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.
Simultaneous localization and mapping: part I
(2006)

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association
A solution to the simultaneous localization and map building (SLAM) problem
(2001)

The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1], [2], [3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including sub-optimal map-building algorithms and map management.
Walk detection and step counting on unconstrained smartphones
(2013)

Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.
 This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.